Origin, occurrence, and biodegradation of long-side-chain alkyl compounds in the environment: a review

Fungal biotransformation of short-chain n-alkylcycloalkanes

The cycloalkanes, comprising up to 45% of the hydrocarbon fraction, occur in crude oil or refined oil products (e.g., gasoline) mainly as alkylated cyclohexane derivatives and have been increasingly found in environmental samples of soil and water. Furthermore, short-chain alkylated cycloalkanes are components of the so-called volatile organic compounds (VOCs). This study highlights the biotransformation of methyl- and ethylcyclohexane by the alkane-assimilating yeast Candida maltosa and the phenol- and benzoate-utilizing yeast Trichosporon mucoides under laboratory conditions. In the course of this biotransformation, we detected 25 different metabolites, which were analyzed by HPLC and GC-MS. The biotransformation process of methylcyclohexane in both yeasts involve (A) ring hydroxylation at different positions (C2, C3, and C4) and subsequent oxidation to ketones as well as (B) oxidation of the alkyl side chain to hydroxylated and acid products. The yeast T. mucoides additionally performs ring hydroxylation at the C1-position and (C) oxidative decarboxylation and (D) aromatization of cyclohexanecarboxylic acid. Both yeasts also oxidized the saturated ring system and the side chain of ethylcyclohexane. However, the cyclohexylacetic acid, which was formed, seemed not to be substrate for aromatization. This is the first report of several new transformation reactions of alkylated cycloalkanes for eukaryotic microorganisms.

QSAR modelling of water quality indices of alkylphenol pollutants

The aim of this study was to determine the degradability of 26 Alkylphenols (APs) by Chemical Oxygen Demand (COD) and/or 5-day Biochemical Oxygen Demand (BOD(5)), and to describe these data from Quantitative Structure-activity Relationships (QSARs). Statistical analysis techniques, such as Multiple Linear Regression (MLR), Principal Component Regression (PCR), Partial Least-Squares (PLS) Regression and Neural Network (NN) were carried out to calibrate and validate four-descriptor QSAR models using two different types of descriptor sets. Stable MLR-QSAR models using Leave-One-Out (LOO) were obtained with high predictability performance: r(2) = 0.924, Q(2)(cv) =0.854 for log (1/BOD) model on 24 APs and r(2) = 0.888, Q(2)(cv) = 0.818 for log (1/COD) on all the studied APs. The MLR models, built with four Dragon descriptors selected by Genetic Algorithm (GA), presented the following performances on 24 APs: r(2) = 0.889, Q(2)(cv) = 0.848 for log (1/BOD(5)) and r(2) = 0.885, Q(2)(cv) = 0.834 for log (1/COD) on 26 compounds. From these results, it is expected that the QSAR models generated could be successfully expanded to predict the biological and chemical activities of structurally diverse AP compounds.